Complex industrial systems such as, for example, power generation systems and chemical, pharmaceutical and refining processing systems, have experienced a need to operate ever more efficiently in order to remain competitive. This need has resulted in the development and deployment of process modeling systems. These modeling systems are used to construct a process model, or flowsheet, of an entire processing plant using equipment or component models provided by the modeling system. These process models are used to design and evaluate new processes, redesign and retrofit existing process plants, and optimize the operation of existing process plants.
Existing flowsheet modeling techniques have been directed to discrete units of plant equipment, rather than to entire plant processes. In certain approaches the operation of individual items of plant equipment predicted by a flowsheet model is attempted to be reconciled with measurements of the equipment's actual operation. Data relating to such actual operation is typically acquired by flow sensors and the like positioned on or near the item of equipment. Such flow sensors vary in their accuracy depending on the material in the stream being monitored, the condition of the stream, and the specific sensing technology employed within the flow sensor. Moreover, the performance of flow sensors may be degraded by obstructions, wear or outright failure. The attendant inaccuracies in the operational data produced by the flow sensors may corrupt the reconciliation of such data with the equipment performance predicted by the flowsheet model, thereby resulting in undesirable erroneous predictions or process control adjustments.
The data reconciliation process often involves minimization of the sum of squared errors between predicted and measured operational parameters. However, the relative accuracy of the sensors used in deriving the error terms is generally not taken into account, which tends to introduce inaccuracies into the reconciliation process. That is, a sensor whose behavior changes due to failure or deterioration may cause incorrect adjusted estimates to be attributed to related sensors during the reconciliation process. Since conventional flowsheet models are not predicated upon operation of entire plant processes, it can be difficult to gauge when predicted operation of individual equipment is inconsistent with realistic operation of an overall process.
Equipment condition has also been attempted to be monitored using flowsheet models directed to individual units of equipment. However, it is generally difficult to determine whether a change in output or other monitored parameter of an individual unit of equipment is properly attributed to a change in the equipment itself or to a change in the applicable process “upstream” of the equipment unit.
In the field of power generation systems, this limitation of existing modeling techniques has proven to be particularly undesirable as concerns with deregulation and operational costs have resulted in efforts to improve system reliability and performance. As is well known, the Rankine cycle power plant, which typically utilizes water as the processed fluid, has been pervasive in the power generation industry for many years. In a Rankine cycle power plant, electrical energy is derived from heat energy through the heating of the processed fluid as it travels through tubular walls and thereby forms a vapor. The vapor is generally superheated to form a high pressure vapor, which is input to a turbine generator to produce electricity.
Other improvements in the efficiency of Rankine cycle power systems have been achieved through technological enhancements, which have enabled the temperatures and pressures of processed fluids to be increased. When reconciliation techniques such as those described above are employed to monitor the performance of such power systems, such techniques are often applied to individual units of equipment or indicia of performance (e.g., turbine efficiency). A dramatic change in such indicia signals that the applicable unit(s) of equipment may be not be operating properly. Again, however, such approaches are premised upon models of only subsets of the equipment utilized in the overall power generation process, and thus are not subject to the constraints which could be imposed upon the Rankine cycle of the process. This makes such approaches inherently uncertain, because it will not be known whether changes in monitored parameters of isolated equipment units are due to equipment degradation or to changes in upstream conditions.